The contribution of arthritis and arthritis disability to nonparticipation in the labor force: a Canadian example.
Bibliographic record
Abstract
OBJECTIVE: To examine the factors affecting labor force participation and understand how arthritis affects labor force participation in a Canadian working population. METHODS: Data from the 1990 Ontario Health Survey population (n = 35,221) were used. Labor force participation was dichotomized as in the labor force and not in the labor force. Stratified logistic regression analyses by sex were carried out to identify factors associated with not being in the labor force, including arthritis, chronic disorders, and sociodemographic and family composition variables. RESULTS: Overall, 6.7% of men and 23.0% of women were not in the labor force compared with 18.6% and 36.0%, respectively, of men and women with arthritis. After controlling for other covariates, disability caused by arthritis was significantly associated with increased risk of being out of the labor force, with odds ratios of 2.70 for men and 1.91 for women. Low education, pain, and nonarthritis disability were also significantly associated with being out of the labor force. The effects of age and family structure on employment were sex dependent. Women were at higher risk at all age groups. Men with dependent children were more likely to work, as were women who lived alone. For women, having dependent children increased the likelihood of not being in the labor force. CONCLUSION: People with arthritis disability were more likely to be out of the labor force. It was not arthritis per se that limited people in labor force participation, but rather the arthritis disabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".